Project Description: Genomewide association studies (GWAS) have identified >4000 genetic loci for a wide range of human traits, but still leaving a large proportion of heritability unexplained. In the post-GWAS era, geneticists are exploiting massively parallel sequencing technologies to study less common (minor allele frequency [MAF] 0.5- 5%) and rare (MAF<0.5%) variants, hereafter together referred to as rare variants for brevity. In the meantime, multiethnic GWAS, recognized as potentially more powerful for gene discovery and fine mapping, are receiving increasing attention from the genetics community. Among the multiethnic populations, admixed populations such as African Americans and Hispanic Americans are particularly attractive because they comprise more than 20% of the US population. These admixed populations offer a unique opportunity for gene mapping because one can utilize admixture linkage disequilibrium (LD) to search for genes underlying diseases that differ strikingly in prevalences across populations. However, little methodological work exists for admixed populations that can accommodate post-GWAS data. The methodological work lags in at least three major areas. First, there are few, if any, genotype imputation methods that are tailored to admixed samples, can accommodate the ever increasing public resources, and the typical mixture of genotyping and sequencing data among the study samples. Imputation will continue to play an essential role as sequencing will remain cost prohibitive for large GWAS collections of samples. Second, there has been no published work on practical issues regarding rare variant imputation in admixed populations. Third, despite the recent rich literature of statistical methods for rare variant association analysis in relatively homogenous populations, the field needs methods that can efficiently analyze rare variants in admixed samples, particularly with imputed or partially imputed data. In this application, we propose the following aims to fill in the above gaps: 1). Develop efficient hidden Markov model and Singular Value Decomposition based methods for haplotype-to-haplotype imputation in admixed populations;2). Assess quality of and provide practical guidelines on rare variants imputation in admixed populations;3). Develop a robust statistical test for the analysis of rare variants in admixed populations;and 4). Develop, distribute and support freely available software packages for the methods developed in this project.

Public Health Relevance

Genomewide association studies (GWAS) have identified >4000 genetic loci for a wide range of human traits, but still leaving a large proportion of heritability unexplained. In the post-GWAS era, geneticists are exploiting massively parallel sequencing technologies to study less common (minor allele frequency [MAF] 0.5- 5%) and rare (MAF<0.5%) variants, hereafter together referred to as rare variants for brevity. In the meantime, multiethnic GWAS, recognized as potentially more powerful for gene discovery and fine mapping, are receiving increasing attention from the genetics community. Among the multiethnic populations, admixed populations such as African Americans and Hispanic Americans are particularly attractive because they comprise more than 20% of the US population. These admixed populations offer a unique opportunity for gene mapping because one can utilize admixture linkage disequilibrium (LD) to search for genes underlying diseases that differ strikingly in prevalences across populations. However, little methodological work exists for admixed populations that can accommodate post-GWAS data. In this application, we will fill in methodological and practical gaps in the genetic analysis of rare variants in admixed populations

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG006703-03
Application #
8634810
Study Section
Special Emphasis Panel (ZRG1-GGG-C (02))
Program Officer
Brooks, Lisa
Project Start
2012-05-16
Project End
2015-02-28
Budget Start
2014-03-01
Budget End
2015-02-28
Support Year
3
Fiscal Year
2014
Total Cost
$308,717
Indirect Cost
$85,855
Name
University of North Carolina Chapel Hill
Department
Genetics
Type
Schools of Medicine
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
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Hui, Daniel; Fang, Zhou; Lin, Jerome et al. (2017) LAIT: a local ancestry inference toolkit. BMC Genet 18:83
Duan, Qing; Xu, Zheng; Raffield, Laura M et al. (2017) A robust and powerful two-step testing procedure for local ancestry adjusted allelic association analysis in admixed populations. Genet Epidemiol :
Xu, Zheng; Zhang, Guosheng; Jin, Fulai et al. (2016) A hidden Markov random field-based Bayesian method for the detection of long-range chromosomal interactions in Hi-C data. Bioinformatics 32:650-6
Naik, Rakhi P; Wilson, James G; Ekunwe, Lynette et al. (2016) Elevated D-dimer levels in African Americans with sickle cell trait. Blood 127:2261-3
Xu, Zheng; Zhang, Guosheng; Duan, Qing et al. (2016) HiView: an integrative genome browser to leverage Hi-C results for the interpretation of GWAS variants. BMC Res Notes 9:159

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